Unified predictive retail eco- system for generating predictions across a range of products

Abstract

Disclosed is a method and system for generating predictions for customers for a range of products in retail sector. The method comprises classifying a product based upon exact values of a decision making feature set to generate a product buy point; b) associating the product to the product buy point and to a number of price ranges; c) partitioning the product buy point into as many affordability partitioned product buy points as the number of price ranges; d) computing a seasonality vector and a frugality vector for the product and for the product buy point based upon an actual sale record of the product; f) unifying the customer preference data from the plurality of sources and generating a customer product matrix; and h) applying a singular vector decomposition technique and a reconstitution technique for find out positive predictions.

I/we claim: 
     
         1 . A method for generating predictions for customers for a range of products in a retail sector, the method comprising:
 classifying a product based upon exact values of a decision making feature set to generate a product buy point, wherein the product buy point is stored as a record;   associating the product to the product buy point and to a number of affordability price ranges;   partitioning the product bus point into as many affordability partitioned product buy points as the number of affordability price ranges;   computing a seasonality vector and a frugality vector for the product and for the product buy point based upon an actual sale records of the product, wherein the seasonality vector is indicative of a time frame or seasons, and wherein the frugality vector is a discount constraint on the product or customer;   receiving customers preference data for the product from a plurality of sources;   unifying the customer preference data of different importance from the plurality of sources;   generating a customer product matrix based upon the unified customer preference data of the customers and the affordability partitioned product buy points wherein the customer product matrix is generated by applying an evolution parameter and a frugality fraction on the unified customer preference data, wherein the evolution parameter is indicative of a customer's changing buying behavior over a period of time; and   applying a singular vector decomposition technique and a reconstitution technique to the customer product matrix to obtain non zero values of the customer product matrix, wherein the non zero values are indicative of positive predictions for the partitioned buy points for a given customer in the customer product matrix.   
     
     
         2 . The method of  claim 1 , wherein the customer product matrix comprises an interest factor indicative of products being added in a shopping cart, or being browsed, or being actually bought by a customer. 
     
     
         3 . The method of  claim 1 , wherein the decision making feature set comprise of different features comprising brand, a price, a material, a style, a color, and a pattern. 
     
     
         4 . The method of  claim 1 , further comprising receiving information of the customer, wherein the information comprises an age group, an occupation, a culture, a tradition, and a geographical area of residence. 
     
     
         5 . The method of  claim 1 , wherein the customer product matrix further comprises a set of affordability partitioned product buy points, wherein the set partitioned buy points are indicative of all products associated with the price ranges of corresponding product buy points. 
     
     
         6 . The method of  claim 1 , wherein the plurality of sources comprises sales history of physical sources, online sales, browsing history of products, and shopping cart items. 
     
     
         7 . A system ( 2002 ) for generating predictions for customers for a range of products in a retail sector, the system comprises:
 a memory ( 2010 ); and   a processor ( 2008 ) coupled to the memory, wherein the processor executes programmed instructions stored in the memory to:
 classify a product based upon exact values of a decision making feature set to generate a product buy point, wherein the product buy point is stored as a record; 
 associate the product to the product buy point and to a number of affordability price ranges; 
 partition the product buy point into as many affordability partitioned product buy points as the number of affordability price ranges; 
 compute a seasonality vector and a frugality vector for the product and for the product buy point based upon an actual sale records of the product, wherein the seasonality vector is indicative of a time frame or seasons, and wherein the frugality vector is a discount constraint on the product or customer; 
 receive customers preference data for the product from a plurality of sources; 
 unify the customer preference data of different importance from the plurality of sources; 
 generate a customer product matrix based upon the unified customer preference data of the customers and the affordability partitioned product buy points, wherein the customer product matrix is generated by applying an evolution parameter and a frugality fraction on the unified customer preference data, wherein the evolution parameter is indicative of a customer's changing buying behavior over a period of time; and 
 apply a singular vector decomposition technique and a reconstitution technique to the customer product matrix to obtain non zero values of the customer product matrix, wherein the non-zero values are indicative of positive predictions for the partitioned buy points for a given customer in the customer product matrix. 
   
     
     
         8 . The system of  claim 7 , wherein the customer product matrix comprises an interest factor indicative of products being added in a shopping cart, or being browsed, or being actually bought by a customer. 
     
     
         9 . The system of  claim 7 , wherein the decision making feature set comprise of different features comprising brand, a price, a material, a style, a color, and a pattern. 
     
     
         10 . The system of  claim 7 , further comprising receiving information of the customer, wherein the information comprises an age group, an occupation, a culture, a tradition, and a geographical area of residence. 
     
     
         11 . The system of  claim 7 , wherein the customer product matrix further comprises a set of affordability partitioned product buy points, wherein the set partitioned buy points are indicative of all products associated with the price ranges of corresponding product buy points. 
     
     
         12 . The system of  claim 7 , wherein the plurality of sources comprises sales history of physical sources, online sales, browsing history of products, and shopping cart items. 
     
     
         13 . A non-transitory computer readable medium embodying a program executable in a computing device for generating predictions for customers for a range of products in a retail sector, the program comprising:
 a program code for classifying a product based upon exact values of a decision making feature set to generate a product buy point, wherein the product buy point is stored as a record;   a program code for associating the product to the product buy point and to a number of affordability price ranges;   a program code for partitioning the product buy point into as man affordability partitioned product buy points as the number of affordability price ranges;   a program code for computing a seasonality vector and a frugality vector for the product and for the product buy point based upon an actual sale records of the product, wherein the seasonality vector is indicative of a time frame or seasons, and wherein the frugality vector is a discount constraint on the product or customer;   a program code for receiving customers preference data for the product from a plurality of sources;   a program code for unifying the customer preference data of different importance from the plurality of sources;   a program code for generating a customer product matrix based upon the unified customer preference data of the customers and the affordability partitioned product buy points, wherein the customer product matrix is generated by applying an evolution parameter and a frugality fraction on the unified customer preference data, wherein the evolution parameter is indicative of a customer's changing buying behavior over a period of time; and   a program code for applying a singular vector decomposition technique and a reconstitution technique to the customer product matrix to obtain non zero values of the customer product matrix, wherein the non-zero values are indicative of positive predictions for the partitioned buy points for a given customer in the customer product matrix.